Method and Apparatus for Inspecting Wind Turbine Blade, And Device And Storage Medium Thereof

ABSTRACT

A method and apparatus for inspecting a wind turbine blade. The method includes: acquiring a sound signal generated by an impingement of wind on the wind turbine blade using a sound acquisition device; generating a frequency spectrogram corresponding to the sound signal; and obtaining a damage recognition result of the wind turbine blade from the frequency spectrogram by performing image recognition on the frequency spectrogram based on a damage recognition model. With the method, a damage type of the wind turbine blade is accurately recognized based on the frequency spectrogram without manual inspection. Therefore, human resources are saved. In addition, the health state of the wind turbine blade can be monitored in real time.

TECHNICAL FIELD

The present disclosure relates to the field of applications, and inparticular, relates to a method and apparatus for inspecting a windturbine blade, and a device and a storage medium thereof.

BACKGROUND

In response to the appeal of the world environmental organization, Chinais committed to the development and usage of environmentally-friendlyenergy, such as wind power generation with mature technologies.

The wind power generation depends on a wind turbine, and wind turbineblades determine the wind catching capacity and efficiency of the windturbine. Therefore, the state monitoring of the wind turbine blades isof great significance. A traditional method for inspecting the windturbine blade is a manual inspection method, in which the wind turbineblade is regularly inspected by a technician by eye observation andsound discrimination.

This manual inspection method has high operation and maintenance costs,and fails to monitor the health state of the wind turbine blade in realtime.

SUMMARY

Embodiments of the present disclosure provide a method and apparatus forinspecting a wind turbine blade, and a device and a storage mediumthereof, which can reduce the operation and maintenance costs of themanual inspection method and monitor the health state of the windturbine blade in real time.

According to an aspect of the present disclosure, a method forinspecting a wind turbine blade is provided, wherein the wind turbineblade is a blade in a wind power generation device, the wind powergeneration device further including a tower provided with a soundacquisition device. The method includes:

acquiring a sound signal generated by an impingement of wind on the windturbine blade using the sound acquisition device, wherein the soundsignal includes a sound signal generated by sliding of air betweenblades in the case that the wind impinges on the wind turbine blade;

generating a frequency spectrogram based on the sound signal; and

obtaining a damage recognition result of the wind turbine blade byperforming image recognition on the frequency spectrogram based on adamage recognition model, wherein the damage recognition model isobtained by training a neural network model.

According to another aspect of the present disclosure, an apparatus forinspecting a wind turbine blade is provided, wherein the wind turbineblade is a blade in a wind power generation device, the wind powergeneration device further including a tower provided with a soundacquisition device. The apparatus includes:

an acquiring module, configured to acquire a sound signal generated byan impingement of wind on the wind turbine blade using the soundacquisition device, wherein the sound signal includes a sound signalgenerated by sliding of air between blades in the case that the windimpinges on the wind turbine blade;

a generating module, configured to generate a frequency spectrogrambased on the sound signal; and

a recognizing module, configured to obtain a damage recognition resultof the wind turbine blade by performing image recognition on thefrequency spectrogram based on a damage recognition model, wherein thedamage recognition model is obtained by training a neural network model.

According to another aspect of the present disclosure, a wind powergeneration device is provided. The wind power generation deviceincludes:

a sound acquisition device; a memory communicably connected to the soundacquisition device; and a processor communicably connected to thememory, wherein

the sound acquisition device is configured to acquire a sound signalgenerated by an impingement of wind on a wind turbine blade of the windpower generation device, and store the sound signal in the memory;

the memory is configured to store an executable instruction and thesound signal; and

the processor is configured to load and execute the executableinstruction stored in the memory to perform the method for inspectingthe wind turbine blade as described above.

In a still further aspect, a non-transitory computer-readable storagemedium is provided. The computer-readable storage medium stores at leastone instruction, at least one program, a code set, or an instruction settherein, wherein the at least one instruction, the at least one program,the code set, or the instruction set, when loaded and executed by aprocessor, causes the processor to perform the method for inspecting thewind turbine blade as described above.

The technical solutions according to the embodiments of the presentdisclosure at least achieve the following beneficial effects:

In the method, the sound signal generated by the impingement of the windon the wind turbine blade is acquired using the sound acquisitiondevice; the frequency spectrogram corresponding to the sound signal isgenerated; and the damage recognition result of the wind turbine bladeis obtained from the frequency spectrogram by performing imagerecognition on the frequency spectrogram based on the damage recognitionmodel. Thus, a damage type of the wind turbine blade is accuratelyrecognized based on the frequency spectrogram without manual inspection.Therefore, human resources are saved. In addition, the health state ofthe wind turbine blade can be monitored in real time. Moreover, in thismethod, the damage of the wind turbine blade is recognized based on thesound signal without depending on any wind turbine operating data, suchthat the machine calculation amount during the detection of the damageof the wind turbine blade is reduced.

BRIEF DESCRIPTION OF THE DRAWINGS

For clearer descriptions of the technical solutions in the embodimentsof the present disclosure, the following briefly introduces theaccompanying drawings required for describing the embodiments.Apparently, the accompanying drawings in the following description showmerely some embodiments of the present disclosure, and a person ofordinary skill in the art may still derive other drawings from theseaccompanying drawings without creative efforts.

FIG. 1 is a structural diagram of a wind power generation systemaccording to one exemplary embodiment of the present disclosure;

FIG. 2 is a flowchart of a method for inspecting a wind turbine bladeaccording to one exemplary embodiment of the present disclosure;

FIG. 3 is a time-domain signal diagram of a sound signal according toone exemplary embodiment of the present disclosure;

FIG. 4 is a frequency spectrogram of a sound signal according to oneexemplary embodiment of the present disclosure;

FIG. 5 is a flowchart of a method for recognizing a damage typeaccording to one exemplary embodiment of the present disclosure;

FIG. 6 is a flowchart of a method for inspecting a wind turbine bladeaccording to another exemplary embodiment of the present disclosure;

FIG. 7 is a block diagram of an apparatus for inspecting a wind turbineblade according to one exemplary embodiment of the present disclosure;and

FIG. 8 is a structural diagram of a server according to one exemplaryembodiment of the present disclosure.

DETAILED DESCRIPTION

For clearer descriptions of the objectives, technical solutions andadvantages in the present disclosure, embodiments of the presentdisclosure are described in detail below in combination with theaccompanying drawings.

Referring to FIG. 1 , a structural diagram of a wind power generationsystem according to one exemplary embodiment of the present disclosureis shown. The wind power generation system includes a wind powergeneration device (i.e., a wind turbine) 120 and a wind turbine bladeinspection device 140.

The wind power generation device 120 includes wind turbine blades 122and a tower 124. Wind impinges on the wind turbine blade 122 to generatewind energy, and the wind power generation device 120 converts the windenergy into electric energy and stores the electric energy into anenergy storage device. The tower 124 is configured to support otherdevice structures of the wind power generation device. For example, thewind turbine blades 122 is connected to a top end of the tower 124 by ahub.

The wind turbine blade inspection device 140 includes a soundacquisition device 142 and a cluster of background servers 144. Thesound acquisition device 142 is disposed on the tower 124.Illustratively, the sound acquisition device 142 is disposed on a towerdoor. Optionally, the sound acquisition device 142 includes a soundsensor or a microphone.

The sound acquisition device 142 is connected to a background server 144over a wired or wireless network. The sound acquisition device 142 isconfigured to acquire a sound signal generated by sliding of air betweenblades in the case that the wind impinges on the wind turbine blade, andtransmits the sound signal to the cluster of background servers 144. Thecluster of background servers 144 is configured to store the soundsignal and load and execute an executable instruction to perform themethod for inspecting the wind turbine blade according to the presentdisclosure.

It should be noted that the sound acquisition device 142 is connected toa processing box. A processor is disposed in the processing box and iscapable of compressing the sound signal. The sound acquisition device142 is further connected to the background server cluster over a wiredor wireless network. The sound acquisition device 142 compresses theacquired sound signal by the processing box and then transmits theprocessed sound signal to the cluster of background servers 144.

In some embodiments, the cluster of background servers 144 is furtherprovided with a display screen for displaying a damage degree and adamage type of the wind turbine blade.

In some embodiments, the cluster of background servers 144 is furtherprovided with an alarm device. When the background server cluster 144determines that the wind turbine blade is damaged, the cluster ofbackground servers 144 controls the alarm device to give an alarm. Insome other embodiments, the cluster of background servers 144 maycontrol the alarm device to give a corresponding alarm according to thedamage type of the wind turbine blade, and different damage types of thewind turbine blade correspond to different alarms.

Referring to FIG. 2 , a flowchart of a method for inspecting a windturbine blade according to one exemplary embodiment of the presentdisclosure is shown. The method is applicable to the wind powergeneration system shown in FIG. 1 and includes the following steps.

In step 201, a sound signal generated by an impingement of wind on thewind turbine blade impinges is acquired using the sound acquisitiondevice.

The sound signal includes a sound signal generated by sliding of airbetween blades in the case that the wind impinges on the wind turbineblade. Illustratively, the sound acquisition device acquires the soundsignal generated by the impingement of the wind on the wind turbineblade, and transmits the sound signal to the processing box. The soundsignal is compressed by the processing box, and then is transmitted tothe background server by the processing box.

The background server stores the sound signal in a memory. Whenexecuting the method for inspecting the wind turbine blade, thebackground server acquires the sound signal generated by the impingementof the wind on the wind turbine blade from the memory.

In step 202, a frequency spectrogram is generated based on the soundsignal.

The background server obtains the frequency spectrogram corresponding tothe sound signal by performing short-time Fourier transform on the soundsignal. Illustratively, different colors may be used to representdifferent sound amplitudes during the drawing of the frequencyspectrogram.

Optionally, the wind power generation device may include m wind turbineblades, each having a corresponding frequency spectrum region, and thebackground server may generate the segmented frequency spectrogram inunits of wind turbine blades, where M is a positive integer.

Exemplary steps are as follows.

1) A signal envelope is extracted from a time domain signal diagramformed by the sound signal by calling a signal analysis algorithm.

The background server extracts the signal envelope from the time domainsignal diagram formed by the sound signal, wherein the signal enveloperefers to a curve that has at least one point tangent to each of a curvefamily in the time domain signal diagram; and determines the position ofa point, where a wave trough appears on the signal envelope, in the timedomain as a segmentation point.

The signal analysis algorithm is configured to analyze the time domainsignal diagram of the sound signal to obtain the signal envelope.Illustratively, the above signal analysis algorithm may include atransfer function, such as a Hilbert transfer function. Optionally, thebackground server extracts the signal envelope from the time domainsignal diagram based on the Hilbert transfer function.

2) The position of the point, where the wave trough is located on thesignal envelope, in the time domain is determined as the segmentationpoint.

Illustratively, as shown in FIG. 3 , the background server generates thetime domain signal diagram 31 based on the sound signal, extracts thesignal envelope 32 from the time domain signal diagram 31 based on theHilbert transfer function, and determines a point 33 where each wavetrough is located on the signal envelope 32. The position of the point33 in the time domain is the segmentation point. In the time domainsignal diagram, a part between the two adjacent segmentation pointsindicates the time domain signal diagram in the case that the windimpinges one wind turbine blade.

3) The sound signal is converted into the frequency spectrogram and thefrequency spectrogram is segmented based on the segmentation point toobtain the segmented frequency spectrogram.

In some embodiments, the background server converts the sound signalinto the frequency spectrogram based on short-time Fourier transform orLaplace transform, that is, the background server converts a time domainsignal of the sound signal into a frequency domain signal based onshort-time Fourier transform or Laplace transform to form the frequencyspectrogram; and segments the frequency spectrogram based on thesegmentation points on a time axis to obtain n frequency spectrumregions of the wind turbine blade, where n is a positive integer. In thefrequency spectrogram, a frequency spectrum region between the twoadjacent segmentation points refers to a frequency spectrum in the casethat the wind impinges on one wind turbine blade.

Illustratively, as shown in FIG. 4 , 8 curves in the frequencyspectrogram 41 are sound signal curves of the wind turbine blade ondifferent frequency bands respectively, and the frequency spectrogram issegmented into 25 frequency spectrum regions based on the segmentationpoints. As the wind turbine blades include three wind turbine blades andthe above 25 frequency spectrum regions correspond to the three windturbine blades respectively. The three continuous frequency spectrumregions correspond to three different wind turbine blades. The(3m−2)^(th) frequency spectrum region is a frequency spectrum region ofthe wind turbine blade A, the (3m−1)^(th) frequency spectrum region is afrequency spectrum region of the wind turbine blade B, and the 3m^(th)frequency spectrum region is a frequency spectrum region of the windturbine blade C, where m is a positive integer.

In step 203, a damage recognition result of the wind turbine blade isobtained by performing image recognition on the frequency spectrogrambased on a damage recognition model.

The damage recognition model is configured in the background server; andthe background server determines a damage type of the wind turbine bladefrom the frequency spectrogram by performing image recognition on thefrequency spectrogram based on the damage recognition mode. Optionally,the background server determines the damage type of the wind turbineblade from the segmented frequency spectrogram by performing imagerecognition on the segmented frequency spectrogram based on the damagerecognition model.

In some embodiments, the damage recognition result includes the damagetype of the wind turbine blade. The damage type includes at least ofblockage of a drainage hole of the wind turbine, cracking of a bladeprotection film, corrosion of a front edge of the blade, fracture of aroot of the blade, blade whistling, and lightning damage.

Illustratively, as shown in FIG. 5 , a flowchart for recognizing thefrequency spectrogram based on the damage recognition model is shown.The background server inputs the frequency spectrogram 51 into aconvolution and pooling layer 52 of the model, and maps the frequencyspectrogram to a feature space by convolution and pooling to obtain animage feature of the frequency spectrogram; inputs the above imagefeature into a feature conversion layer 53 for feature conversion toobtain the converted image feature; then inputs the converted imagefeature into a fully-connected layer 54, and obtains a featureclassification result by recognizing and classifying the converted imagefeature by the fully-connected layer 54; and finally obtains the damagerecognition result by normalizing the feature classification resultbased on an output layer 55. The damage recognition result includes thedamage type of the wind turbine blade.

It should be noted that the above damage recognition model is obtainedby training a neural network model. Illustratively, a training processof the above damage recognition model is as follows.

1) A frequency spectrogram sample is acquired.

The frequency spectrogram sample is a frequency spectrogram set obtainedby acquiring historical frequency spectrograms, and the frequencyspectrogram sample set includes images corresponding to different damagetypes in different historical frequency spectrograms. Damage positionsand sample damage types are correspondingly marked in the abovefrequency spectrogram sample.

2) The frequency spectrogram sample is input into the neural networkmodel for image recognition to obtain the recognized damage type of thedamage position.

The background server inputs the acquired frequency spectrogram sampleinto the neural network model for obtaining the determined damage typecorresponding to each damage position by performing image recognition onthe frequency spectrogram sample based on the neural network model.

In some embodiments, the above neural network model may be a longshort-term memory model, a convolutional neural network model, afeedforward neural network model or the like. The type of the neuralnetwork model is not limited in this embodiment.

3) Error back propagation training is performed based on the recognizeddamage type and the sample damage type to train a recognition capacityof the neural network model against the damage type of the wind turbineblade, and hence the damage recognition model is obtained.

The neutral network model calculates an error between the recognizeddamage type and the sample damage type, performs error back propagation,and adjusts its own model parameter. Thus, the recognition capacity ofthe neural network model against the damage type of the wind turbineblade is trained and finally the damage recognition model is obtained.

In summary, in the method for inspecting the wind turbine bladeaccording to the present disclosure, the sound signal generated by theimpingement of the wind on the wind turbine blade is acquired using thesound acquisition device; the frequency spectrogram corresponding to thesound signal is generated; and the damage recognition result of the windturbine blade is obtained from the frequency spectrogram by performingimage recognition on the frequency spectrogram based on the damagerecognition model. Thus, the damage type of the wind turbine blade isaccurately recognized based on the frequency spectrogram without manualinspection. Therefore, human resources are saved. In addition, thehealth state of the wind turbine blade can be monitored in real time.Moreover, in this method, the damage of the wind turbine blade isrecognized based on the sound signal without depending on any windturbine operating data, such that the machine calculation amount duringthe detection of the damage of the wind turbine blade is reduced.

It should be noted that the method for inspecting the wind turbine bladeaccording to the present disclosure is to find the damage of the windturbine blade immediately in the process that the wind turbine bladesrotates, and confirm the damage type. Therefore, before the damage typeof the wind turbine blade is recognized, it is possible to determinefirstly whether the wind turbine blade is damaged. Illustratively, basedon FIG. 2 , step 203 may include sub-steps 2031 and 2032. As shown inFIG. 6 , the steps are as follows.

In sub-step 2031, a sound spectrum difference factor in response to theimpingement of the wind on the wind turbine blade is calculated based onthe segmented frequency spectrogram.

The above sound spectrum difference factor represents the damage degreeof the wind turbine blade. In some embodiments, the sound spectrumdifference factor in response to the impingement of the wind on the windturbine blade is calculated based on the segmented frequencyspectrogram.

In some embodiments, the segmented frequency spectrogram includesfrequency spectrum regions of n wind turbine blades after segmentation,where n is a positive integer. Illustratively, exemplary steps that thebackground server calculates, based on the frequency spectrum regions ofthe n wind turbine blades, the sound spectrum difference factor inresponse to the impingement of the wind on the wind turbine blade are asfollows.

1) Signal peaks in the n frequency spectrum regions are extracted.

2) A time domain factor and a frequency domain factor of the soundsignal are calculated based on the signal peaks of the n frequencyspectrum regions.

Specifically, m wind turbine blades are disposed on the wind powergeneration device. In some embodiments, the background server calculatesthe time domain factor of the sound signal: the background serverfirstly determines a median of signal peaks of at least two of thefrequency spectrum regions corresponding to each of the wind turbineblades; next determines a maximum peak and a minimum peak from m medianscorresponding to the m wind turbine blades; and finally determines aratio of the maximum peak to the minimum peak as the time domain factor.

Illustratively, if m is 3, the background server chooses the curve oncertain frequency band in the frequency spectrogram to calculate thetime domain factor. For example, the background server chooses the curveon the frequency band of (−0.008)−(−0.006) in the frequency spectrogram41 shown in FIG. 4 to calculate the time domain factor, or chooses thecurve of the sound signal on the whole frequency band (not shown) tocalculate the time domain factor. The wind turbine blades include threewind turbine blades. If 25 frequency spectrum regions are included inthe selected curve, the background server determines a signal peak ineach frequency spectrum region, with a total of 25 signal peaks, inwhich 9 signal peaks are signal peaks of the wind turbine blade A in thecorresponding (3m−2)^(th) frequency spectrum region, 8 signal peaks aresignal peaks of the wind turbine blade B in the corresponding(3m−1)^(th) frequency spectrum region, and 8 signal peaks are signalpeaks of the wind turbine blade C in the corresponding 3mth frequencyspectrum region. The background server determines corresponding mediansa, b and c from the signal peaks of the wind turbine blades A, B and C,determines the maximum peak and the minimum peak from the medians a, band c, and finally determines the ratio of the maximum peak to theminimum peak as the time domain factor. For example, if the maximum peakis a and the minimum peak is c, the time domain factor is a/c.

In some embodiments, the background server calculates the frequencydomain factor of the sound signal: the background server firstlyacquires the maximum peak from signal peaks of each two adjacentfrequency spectrum regions of m frequency spectrum regions anddetermines the maximum peak as a candidate peak; and then determines amedian of at least two of the candidate peaks as the frequency domainfactor; or the background server calculates a relative entropy, i.e.,Kullback-Leibler (KL) divergence, between signal distribution andtheoretical distribution in the segmented frequency spectrogram, anddetermines the KL divergence as the frequency domain factor.

Illustratively, as shown in FIG. 4 , the background server marks the 25frequency spectrum regions as 1 to 25 from left to right, acquires themaximum peaks from the corresponding signal peaks of adjacent frequencyspectrum regions 1-3, 2-4, 3-5, . . . , 23-25, with a total of 23 signalpeaks, and determines the median from the above 23 signal peaks. Thismedian is one frequency domain factor.

The background server calculates the KL divergence between signaldistribution and theoretical signal distribution in the segmentedfrequency spectrogram, and determines the KL divergence as anotherfrequency domain factor.

It should be noted that the above frequency domain factor represents adistribution feature of the sound signal in a frequency domain. Thepresent disclosure provides two methods for calculating the distributionfeature of the sound signal in the frequency domain, but in the presentdisclosure, the method for calculating the distribution feature of thesound signal in the frequency domain is not limited to the two methodsprovided above.

3) A weighted average of the time domain factor and the frequency domainfactor is determined as the sound spectrum difference factor.

Illustratively, the background server obtains one time domain factor andtwo frequency domain factors by calculation, then calculates theweighted average of the one time domain factor and the two frequencydomain factors, and determines the above weighted average as the soundspectrum difference factor.

It should also be noted that the background server further filters thesound signal by a filter to obtain the filtered sound signal, generatesthe frequency spectrogram based on the filtered sound signal and thengenerates the frequency spectrum difference factor.

In sub-step 2032, whether the sound spectrum difference factor isgreater than a difference threshold is determined.

The difference threshold is disposed in the background server and isconfigured to determine whether the wind turbine blade is damaged. Inthe case that the sound spectrum difference factor is greater than thedifference threshold, the wind turbine blade is damaged and sub-step2033 is performed; and in the case that the sound spectrum differencefactor is less than or equal to the difference threshold, the windturbine blade is not damaged and the process returns to step 201.

In sub-step 2033, the damage recognition result of the wind turbineblade is obtained by performing image recognition on the segmentedfrequency spectrogram based on the damage recognition model.

In summary, in the method for inspecting the wind turbine bladeaccording to this embodiment, before the damage type of the wind turbineblade is recognized, firstly whether the wind turbine blade is damagedis determined based on the sound spectrum difference factor; and when itis determined that the wind turbine blade is damaged, the damage type isrecognized. Thus, the probability of recognizing the damage type basedon the damage recognition model is increased, and the invalidrecognition of the damage type of the wind turbine blade by the damagerecognition model is avoided.

It should also be noted that a range threshold may also be set in thebackground server and different range thresholds correspond to differentdamage degrees. In the case that the sound spectrum difference factor isgreater than the difference threshold, the background server determinesthe damage degree of the wind turbine blade based on the range thresholdto which the sound spectrum difference factor belongs.

Illustratively, a first range threshold, a second range threshold and athird range threshold are set in the background server. When the soundspectrum difference factor belongs to the first range threshold, thebackground server determines that the damage degree of the wind turbineblade is mild; when the sound spectrum difference factor belongs to thesecond range threshold, the background server determines that the damagedegree of the wind turbine blade is moderate; and when the soundspectrum difference factor belongs to the third range threshold, thebackground server determines that the damage degree of the wind turbineblade is severe. Values in the range threshold are greater than thedifference threshold.

The background server outputs the damage degree of the wind turbineblade while outputting the damage type of the wind turbine blade.Optionally, the output damage degree may be determined by the backgroundserver based on set damage degree levels, or may be the sound spectrumdifference factor directly.

With this method, a user can clearly know the damage degree and thedamage type of the wind turbine blade from the output result.

It should also be noted that in the process that the background serverexecutes the method for inspecting the wind turbine blade, the soundacquisition device acquires the sound signal with a preset durationevery time, and correspondingly generates a file for storing the abovesound signal with the preset duration. For example, each file includesthe sound signal with the duration of 43 seconds. When calculating thesound spectrum difference factor, the background server acquires thesound signal with the duration of 43 seconds from one file. However, thesignal quality of the sound signals stored in the above various files isdifferent, and there are some files, in which the overall signal qualityis poor. This affects the result of the sound spectrum differencefactor. Therefore, in the process of the calculating the sound spectrumdifference factor, the background server firstly determines the signalquality of the sound signal.

Illustratively, after the segmented frequency spectrogram is obtained,the background server determines the signal quality of the sound signalbased on the segmented frequency spectrogram as follows.

(1) A signal-to-noise ratio of the sound signal is calculated based onthe segmented frequency spectrogram.

The sound acquisition device acquires an original sound signal generatedby the impingement of the wind on the wind turbine blade. In anacquisition process, an additional signal, i.e., noise that is notpresent in the original sound signal is mixed into the original soundsignal. The signal-to-noise ratio refers to a ratio of the acquiredoriginal sound signal to the noise. After the segmentation of thefrequency spectrogram is completed, the background server calculates thesignal-to-noise ratio of the sound signal based on the segmentedfrequency spectrogram.

(2) Whether the signal-to-noise ratio is greater than a signal-to-noiseratio threshold is determined.

The signal-to-noise ratio threshold is set in the background server andthe background server determines whether the signal-to-noise ratio isgreater than the signal-to-noise ratio threshold. In the case that thesignal-to-noise ratio is greater than the signal-to-noise ratiothreshold, step (3) is performed; and in the case that thesignal-to-noise ratio is less than or equal to the signal-to-noise ratiothreshold, step (4) is performed and the process returns to step 201.

(3) The sound spectrum difference factor in response to the impingementof the wind on the wind turbine blade is calculated based on thesegmented frequency spectrogram.

(4) It is determined that the sound acquisition device encounters afailure.

In the wind power generation system, the sound signal generated by theimpingement of the wind on the wind turbine blade is acquired using thesound acquisition device and a communication state of the soundacquisition device directly affects the quality of the acquired soundsignal. Therefore, the background server detects the communication stateof the sound acquisition device. Illustratively, the background serverdetects the communication state of the sound acquisition device in realtime based on the signal quality of the acquired sound signal. Forexample, the background server determines the communication state of thesound acquisition device based on the signal-to-noise ratio of theacquired sound signal, or presence or absence of the sound signal. Whenthere is the sound signal and the sound signal has high quality, itindicates that the sound acquisition device is in a health state. Whenthere is no sound signal or the sound signal is poor, it indicates thatthe sound acquisition device is in an unhealth state and needs to bemaintained to ensure the quality of the acquired sound signal.Therefore, the damage condition of the wind turbine blade can bedetermined accurately.

With an example that the health state of the sound acquisition device isdetected based on the signal-to-noise ratio, in the case that thesignal-to-noise ratio is less than or equal to the signal-to-noise ratiothreshold, there is a large amount of noise in the sound signal acquiredusing the sound acquisition device and the sound signal has poorquality, and thus it is determined that the sound acquisition deviceencounters the failure; and in the case that the signal-to-noise ratiois not less than or equal to the signal-to-noise ratio threshold, thesound acquisition device is in the health state. It should also be notedthat there is randomness when the signal-to-noise ratio is less than thesignal-to-noise ratio threshold at one time. Therefore, if it isdetermined that the signal-to-noise ratio calculated after sound signalsare reacquired for i consecutive times is less than the signal-to-noiseratio threshold, it is determined that the sound acquisition deviceencounters the failure, where i is a positive integer.

In summary, in the method for inspecting the wind turbine bladeaccording to this embodiment, it is ensured that the sound signal forcalculating the sound spectrum difference factor has high quality basedon the quality detection of the sound signal. Thus, the accuracy of thecalculated sound spectrum difference factor is ensured. In addition, inthis method, the health state of the sound acquisition device ismonitored in real time, such that the user can immediately know when thesound acquisition device is abnormal and then performs maintenance.

It should also be noted that an alarm system is further disposed in thebackground server.

When the background server recognizes that the wind turbine blade isdamaged, the background server gives an alarm. When the backgroundserver determines that the sound acquisition device is abnormal, thebackground server gives an alarm.

In some embodiments, different damage types of the wind turbine bladecorrespond to different alarms and the background server gives acorresponding alarm based on the damage type of the wind turbine blade.

In this method, when the damage of the wind turbine blade or the soundacquisition device is recognized, an alarm is given immediately to warnthe user about maintenance punctually. As such, the device may berepaired punctually and greater loss is avoided.

Referring to FIG. 7 , a block diagram of an apparatus for inspecting awind turbine blade according to one exemplary embodiment of the presentdisclosure is shown. The wind turbine blade is a blade in a wind powergeneration device and the wind power generation device further includesa tower provided with a sound acquisition device. The apparatus isimplemented as all or part of a server by software, hardware, or acombination thereof.

The apparatus includes: an acquiring module 301, configured to acquire asound signal generated by an impingement of wind on the wind turbineblade using the sound acquisition device, wherein the sound signalincludes a sound signal generated by sliding of air between blades inthe case that the wind impinges on the wind turbine blade; a generationmodule 302, configured to generate a frequency spectrogram based on thesound signal; and a recognition module 303, configured to obtain adamage recognition result of the wind turbine blade by performing imagerecognition on the frequency spectrogram based on a damage recognitionmodel, wherein the damage recognition model is obtained by training aneural network model.

In some embodiments, the generation module 302 includes: an extractionsub-module 3021, configured to extract a signal envelope from a timedomain signal formed by the sound signal by calling a signal analysisalgorithm; a determination sub-module 3022, configured to determine aposition of a point, where a wave trough appears on the signal envelope,in the time domain as a segmentation point; and a generation sub-module3023, configured to convert the sound signal into the frequencyspectrogram, and segment the frequency spectrogram based on thesegmentation point to obtain the segmented frequency spectrogram.

In some embodiments, the recognition module 303 includes: a calculationsub-module 3031, configured to calculate, based on the segmentedfrequency spectrogram, a sound spectrum difference factor in response tothe impingement of the wind on the wind turbine blade, wherein the soundspectrum difference factor represents a damage degree of the windturbine blade; and a recognition sub-module 3032, configured to obtainthe damage recognition result of the wind turbine blade by performingimage recognition on the segmented frequency spectrogram based on thedamage recognition model in the case that the sound spectrum differencefactor is greater than a difference threshold.

In some embodiments, the segmented frequency spectrogram includesfrequency spectrum regions of n wind turbine blades after segmentation,where n is a positive integer.

The calculation sub-module 3031 is configured to extract signal peaks inthe n frequency spectrum regions; calculate a time domain factor and afrequency domain factor of the sound signal based on the signal peaks ofthe n frequency spectrum regions; and determine a weighted average ofthe time domain factor and the frequency domain factor as the soundspectrum difference factor.

In some embodiments, m wind turbine blades are disposed on the windpower generation device, where m is a positive integer.

The calculation sub-module 3031 is configured to determine a median ofsignal peaks of at least two of the frequency spectrum regionscorresponding to each of the wind turbine blades; determine a maximumpeak and a minimum peak from m medians corresponding to the m windturbine blades; and determine a ratio of the maximum peak to the minimumpeak as the time domain factor.

In some embodiments, m wind turbine blades are disposed on the windpower generation device, where m is a positive integer.

The calculation sub-module 3031 is configured to acquire the maximumpeak from signal peaks of each m adjacent frequency spectrum regions anddetermine the maximum peak as a candidate peak, where m is a positiveinteger; and determine a median of at least two of the candidate peaksas the frequency domain factor; or calculate a relative entropy betweensignal distribution and theoretical distribution in the segmentedfrequency spectrogram, and determine the relative entropy as thefrequency domain factor.

In some embodiments, the calculation sub-module 3031 is configured tocalculate a signal-to-noise ratio of the sound signal based on thesegmented frequency spectrogram; calculate, based on the segmentedfrequency spectrogram, the sound spectrum difference factor in responseto the impingement of the wind on the wind turbine blade in the casethat the signal-to-noise ratio is greater than a signal-to-noise ratiothreshold; and determine that the sound acquisition device encounters afailure in the case that the signal-to-noise ratio is less than thesignal-to-noise ratio threshold.

In summary, in the apparatus for inspecting the wind turbine bladeaccording to this embodiment, the sound signal generated by theimpingement of the wind on the wind turbine blade is acquired using thesound acquisition device; the frequency spectrogram corresponding to thesound signal is generated; and the damage recognition result of the windturbine blande is obtained from the frequency spectrogram by performingimage recognition on the frequency spectrogram based on the damagerecognition model. Thus, the damage type of the wind turbine blade isaccurately determined based on the frequency spectrogram without manualinspection.

Therefore, human resources are saved. In addition, the health state ofthe wind turbine blade can be monitored in real time. Moreover, in theapparatus, the damage of the wind turbine blade is recognized based onthe sound signal without depending on any wind turbine operating data.Thus, the machine calculation amount during the detection of the damageof the wind turbine blade is reduced.

Referring to FIG. 8 , a structural diagram of a server according to oneexemplary embodiment of the present disclosure is shown. The server isconfigured to perform the method for inspecting the wind turbine bladeaccording to the above embodiment.

Specifically, the server 400 includes a central processing unit (CPU)401, a system memory 404 including a random-access memory (RAM) 402 anda read-only memory (ROM) 403, and a system bus 405 connecting the systemmemory 404 and the central processing unit 401. The server 400 furtherincludes a basic input/output system (I/O system) 406 which helpsinformation transmission between various components within a computer,and a high-capacity storage device 407 for storing an operating system413, an application 414 and other program modules 415.

The basic input/output system 406 includes a display 408 for displayinginformation and an input device 409, such as a mouse and a keyboard, forinputting information by a user. Both the display 408 and the inputdevice 409 are connected to the central processing unit 401 by aninput/output controller 410 connected to the system bus 405. The basicinput/output system 406 may further include the input/output controller410 for receiving and processing input from a plurality of otherdevices, such as the keyboard, the mouse, or an electronic stylus.Similarly, the input/output controller 410 further provides output tothe display, a printer, or other types of output devices.

The high-capacity storage device 407 is connected to the centralprocessing unit 401 by a high-capacity storage controller (not shown)connected to the system bus 405. The high-capacity storage device 407and a computer-readable medium associated therewith provide non-volatilestorage for the server 400. That is, the high-capacity storage device407 may include the computer-readable medium (not shown), such as a harddisk or a compact disc read-only memory (CD-ROM) driver.

Without loss of generality, the computer-readable medium may include acomputer storage medium and a communication medium. The computer storagemedium includes volatile and non-volatile, removable and non-removablemedia implemented in any method or technology for storage of informationsuch as a computer-readable instruction, a data structure, a programmodule or other data. The computer storage medium includes a RAM, a ROM,an erasable programmable read-only memory (EPROM), an electricallyerasable programmable read-only memory (EEPROM), a flash memory or othersolid-state storage devices; a CD-ROM, a digital versatile disc (DVD) orother optical storage devices; and a tape cartridge, a magnetic tape, adisk storage or other magnetic storage devices. A person skilled in theart would know that the computer storage medium is not limited to aboveones. The above system memory 404 and the high-capacity storage device407 may be collectively referred to as the memory.

According to various embodiments of the present disclosure, the server400 may also be connected to a remote computer on a network over thenetwork, such as the Internet, for operation. That is, the server 400may be connected to the network 412 by a network interface unit 411connected to the system bus 405, or may be connected to other types ofnetworks or remote computer systems (not shown) with the networkinterface unit 411.

Serial numbers of the above embodiments of the present disclosure areonly for the purpose of description, but do not represent the quality ofthe embodiments.

A person of ordinary skill in the art may understand that all or part ofthe steps in the above embodiments can be completed by hardware, or byrelevant hardware instructed by a program stored in a computer-readablestorage medium, such as a read-only memory, a disk, or an optical disc.

Described above are example embodiments of the present disclosure, andare not intended to limit the present disclosure. Any modifications,equivalent replacements, improvements and the like made within thespirit and principles of the present disclosure should be includedwithin the scope of protection of the present disclosure.

1. A method for inspecting a wind turbine blade, wherein the windturbine blade is a blade in a wind power generation device, the windpower generation device further comprising a tower provided with a soundacquisition device that is mounted away from the wind turbine blade; andthe method comprises: acquiring a sound signal in response to animpingement of wind on the wind turbine blade using the soundacquisition device, wherein the sound signal comprises a sound signalgenerated by sliding of air between blades in the case that the windimpinges on the wind turbine blade; generating a frequency spectrogrambased on the sound signal; and obtaining a damage recognition result ofthe wind turbine blade by performing image recognition on the frequencyspectrogram based on a damage recognition model, wherein the damagerecognition model is obtained by training a neural network model.
 2. Themethod according to claim 1, wherein generating the frequencyspectrogram based on the sound signal comprises: extracting a signalenvelope from a time domain signal diagram formed by the sound signal bycalling a signal analysis algorithm; determining a position of a point,where a wave trough appears on the signal envelope, in a time domain asa segmentation point; and converting the sound signal into the frequencyspectrogram, and obtaining a segmented frequency spectrogram bysegmenting the frequency spectrogram based on the segmentation point. 3.The method according to claim 2, wherein obtaining the damagerecognition result of the wind turbine blade by performing imagerecognition on the frequency spectrogram based on the damage recognitionmodel comprises: calculating a sound spectrum difference factor inresponse to in response to the impingement of wind on the wind turbineblade based on the segmented frequency spectrogram, wherein the soundspectrum difference factor represents a damage degree of the windturbine blade; and obtaining the damage recognition result of the windturbine blade by performing image recognition on the segmented frequencyspectrogram based on the damage recognition model in the case that thesound spectrum difference factor is greater than a difference threshold.4. The method according to claim 3, wherein the segmented frequencyspectrogram comprises frequency spectrum regions of n wind turbineblades after segmentation, where n is a positive integer; andcalculating the sound spectrum difference factor in response to theimpingement of the wind on the wind turbine blade based on the segmentedfrequency spectrogram comprises: extracting signal peaks in n frequencyspectrum regions; calculating a time domain factor and a frequencydomain factor of the sound signal based on the signal peaks of the nfrequency spectrum regions; and determining a weighted average of thetime domain factor and the frequency domain factor as the sound spectrumdifference factor.
 5. The method according to claim 4, wherein m windturbine blades are disposed on the wind power generation device, where mis a positive integer; and calculating the time domain factor of thesound signal based on the signal peaks of the n frequency spectrumregions comprises: determining a median of signal peaks of at least twoof the frequency spectrum regions corresponding to each of the windturbine blades; determining a maximum peak and a minimum peak from mmedians corresponding to the m wind turbine blades; and determining aratio of the maximum peak to the minimum peak as the time domain factor.6. The method according to claim 4, wherein m wind turbine blades aredisposed on the wind power generation device, where m is a positiveinteger; and calculating the frequency domain factor of the sound signalbased on the signal peaks of the n frequency spectrum regions comprises:acquiring a maximum peak from signal peaks of each two adjacentfrequency spectrum regions of m frequency spectrum regions anddetermining the maximum peak as a candidate peak, where m is a positiveinteger; and determining a median of at least two of the candidate peaksas the frequency domain factor; or calculating a relative entropybetween signal distribution and theoretical distribution in thesegmented frequency spectrogram, and determining the relative entropy asthe frequency domain factor.
 7. The method according to claim 3, whereincalculating the sound spectrum difference factor in response to theimpingement of the wind on the wind turbine blade based on the segmentedfrequency spectrogram comprises: calculating a signal-to-noise ratio ofthe sound signal based on the segmented frequency spectrogram;calculating the sound spectrum difference factor in response to theimpingement of the wind on the wind turbine blade based on the segmentedfrequency spectrogram in the case that the signal-to-noise ratio isgreater than a signal-to-noise ratio threshold; and determining that thesound acquisition device encounters a failure in the case that thesignal-to-noise ratio is less than the signal-to-noise ratio threshold.8. An apparatus for inspecting a wind turbine blade, wherein the windturbine blade is a blade in a wind power generation device, the windpower generation device further comprising a tower provided with a soundacquisition device that is mounted away from the wind turbine blade; andthe apparatus comprises: an acquiring module, configured to acquire asound signal generated by an impingement of wind on the wind turbineblade using the sound acquisition device, wherein the sound signalcomprises a sound signal generated by sliding of air between blades inthe case that the wind impinges on the wind turbine blade; a generatingmodule, configured to generate a frequency spectrogram based on thesound signal; and a recognizing module, configured to obtain a damagerecognition result of the wind turbine blade by performing imagerecognition on the frequency spectrogram based on a damage recognitionmodel, wherein the damage recognition model is obtained by training aneural network model.
 9. A wind power generation device, comprising: asound acquisition device; a memory communicably connected to the soundacquisition device; and a processor communicably connected to thememory; wherein the sound acquisition device is configured to acquire asound signal in response to an impingement of wind on the wind turbineblade of the wind power generation device, and store the sound signal inthe memory; the memory is configured to store an executable instructionand the sound signal; and the processor is configured to load andexecute the executable instruction stored in the memory to perform amethod for inspecting the wind turbine blade, wherein the wind turbineblade is a blade in a wind power generation device, the wind powergeneration device further comprising a tower provided with a soundacquisition device that is mounted away from the wind turbine blade themethod comprising: acquiring a sound signal in response to animpingement of wind on the wind turbine blade using the soundacquisition device, wherein the sound signal comprises a sound signalgenerated by sliding of air between blades in the case that the windimpinges on the wind turbine blade; generating a frequency spectrogrambased on the sound signal; and obtaining a damage recognition result ofthe wind turbine blade by performing image recognition on the frequencyspectrogram based on a damage recognition model, wherein the damagerecognition model is obtained by training a neural network model.
 10. Anon-transitory computer-readable storage medium storing at least oneinstruction, at least one program, a code set, or an instruction settherein, wherein the at least one instruction, the at least one program,the code set, or the instruction set, when loaded and executed by aprocessor, causes the processor to perform a method for inspecting thewind turbine blade, wherein the wind turbine blade is a blade in a windpower generation device, the wind power generation device furthercomprising a tower provided with a sound acquisition device that ismounted away from the wind turbine blade, the method comprising:acquiring a sound signal in response to an impingement of wind on thewind turbine blade using the sound acquisition device, wherein the soundsignal comprises a sound signal generated by sliding of air betweenblades in the case that the wind impinges on the wind turbine blade;generating a frequency spectrogram based on the sound signal; andobtaining a damage recognition result of the wind turbine blade byperforming image recognition on the frequency spectrogram based on adamage recognition model, wherein the damage recognition model isobtained by training a neural network model.